Automatic cleaning in acoustic ejection mass spectrometry: Enhancing the system robustness for large-scale high-throughput analysis of complex samples
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The rapid evolution of high-throughput mass spectrometry (HT-MS) technologies has positioned MS as a pivotal analytical tool across diverse disciplines. Its significance is particularly pronounced in high-throughput drug discovery and development, where MS plays a critical role throughout various phases. Acoustic ejection mass spectrometry (AEMS) is a recent addition to the HT-MS landscape, showcasing a balanced performance high analytical throughput and high data quality. Particularly, AEMS's in-line dilution feature allows the direct analysis of large-scale, complex reaction solutions without the need for sample cleanup, making it a popular choice for large-scale high-throughput screenings. However, the substantial volume of complex matrix introduces concerns about system robustness, specifically regarding the potential clogging of the sample transfer line. This study addresses this challenge by introducing an integrated automatic washing feature to the AEMS system. This enhancement significantly improves system robustness without imposing any additional demands on assay execution time. Demonstrating an extended electrode lifetime, the cleaning approach proves effective in maintaining system performance over prolonged periods, showcasing its potential for continuous large-sample-scale high-throughput analysis applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it